A100 GPU Rental

The A100 80GB model has double the GPU memory (80GB vs 40GB), allowing larger batch sizes and bigger models. It also features improved memory bandwidth (2.0 TB/s vs 1.6 TB/s). Spheron offers the 80GB variant for maximum flexibility. The 80GB version is ideal for large language models, high-resolution image processing, and workloads requiring substantial GPU memory.

Absolutely! The A100 is excellent for inference with support for mixed precision (FP16, INT8) and multi-instance GPU (MIG) technology that allows you to partition a single A100 into up to 7 isolated instances. This makes it very cost-effective for serving multiple models concurrently. For pure inference at scale, also consider our L40S options.

All major ML frameworks are fully supported and optimized for A100: PyTorch, TensorFlow, JAX, MXNet, ONNX Runtime, and Triton Inference Server. NVIDIA provides optimized containers for all frameworks with CUDA 11.8+, cuDNN 8.9+, and NCCL for distributed training. We also support RAPIDS for GPU-accelerated data science.

H100 is the newer generation offering 3-4x better performance for transformer models and LLMs, with Transformer Engine and FP8 support. However, A100 provides excellent price-performance for most workloads and is 25% cheaper per hour. For established model architectures, computer vision, and general ML training, A100 remains an excellent choice.

Yes! Spheron supports multi-GPU configurations up to 8x A100 in a single server. For distributed training across nodes, we provide DC grade networking optimized for frameworks like PyTorch DDP, Horovod, and DeepSpeed. NCCL is pre-configured for efficient gradient synchronization.

Spheron's marketplace offers multiple A100 configurations from different providers. For example, a typical A100 instance includes: 80GB GPU memory, 100GB system RAM, 14 vCPUs, 625GB NVMe SSD storage, high-bandwidth networking, pre-installed CUDA drivers, and your choice of ML framework containers. All instances provide root access so you can install any additional software you need. Configurations vary by provider to match different workload requirements.

A100 instances typically provision in 45-75 seconds. Our infrastructure maintains warm pools of GPUs for instant availability. You can go from clicking 'Deploy' to running your training script in under 2 minutes using our Spheron app.

Yes! For sustained workloads or multiple GPUs, we offer custom pricing. Contact our sales team for volume discounts, reserved capacity, and dedicated clusters. We work with startups, enterprises, and research institutions to provide flexible pricing. Book a call with our team

Our team provides technical support to help optimize your GPU infrastructure. We can assist with cost optimization, infrastructure audits, and troubleshooting issue with GPU VM and bare metal servers. Enterprise customers get dedicated Slack channels and architecture review sessions. Book a call with our team

Yes, Spheron offers Spot instances for A100 at significantly reduced rates (up to 70% savings). However, Spot instances can be interrupted when demand increases. Key risks include: potential job interruption during training/inference, loss of unsaved state or checkpoints, and need to restart from last saved checkpoint. Best practices: implement frequent checkpointing (every 15-30 minutes), use Spot for fault-tolerant workloads, save model weights to persistent storage regularly, and consider Spot for development/testing rather than production inference. For critical production workloads, we recommend dedicated instances with SLA guarantees.